Is Cohere Down? How to Check Cohere API Status in Real-Time
Is Cohere Down? How to Check Cohere API Status in Real-Time
Quick Answer: To check if Cohere is down, visit apistatuscheck.com/api/cohere for real-time monitoring, or check the official status.cohere.com page. Common signs include embedding API failures, rerank timeouts, chat/generate errors, rate limiting issues, and authentication failures.
When your RAG pipeline suddenly stops generating embeddings or your semantic search breaks, every second of downtime impacts user experience and business operations. Cohere powers enterprise AI applications with state-of-the-art language models for embeddings, reranking, text generation, and chat. Whether you're seeing 500 errors, timeout exceptions, or authentication failures, knowing how to quickly verify Cohere's status can save critical troubleshooting time and help you make informed decisions about your AI infrastructure.
How to Check Cohere Status in Real-Time
1. API Status Check (Fastest Method)
The quickest way to verify Cohere's operational status is through apistatuscheck.com/api/cohere. This real-time monitoring service:
- Tests actual API endpoints every 60 seconds across all Cohere services
- Shows response times and latency trends for embed, rerank, and generate endpoints
- Tracks historical uptime over 30/60/90 days
- Provides instant alerts when issues are detected
- Monitors multiple regions (US, EU)
- Tracks model-specific availability (embed-english-v3.0, rerank-english-v3.0, command-r-plus)
Unlike status pages that rely on manual updates, API Status Check performs active health checks against Cohere's production endpoints, giving you the most accurate real-time picture of service availability.
2. Official Cohere Status Page
Cohere maintains status.cohere.com as their official communication channel for service incidents. The page displays:
- Current operational status for all services (Embed API, Rerank API, Generate API, Chat API)
- Active incidents and investigations
- Scheduled maintenance windows
- Historical incident reports
- Model-specific status updates
- API dashboard availability
Pro tip: Subscribe to status updates via email or webhook on the status page to receive immediate notifications when incidents occur.
3. Test API Endpoints Directly
For developers, making a test API call can quickly confirm connectivity:
import cohere
# Initialize client
co = cohere.Client('YOUR_API_KEY')
# Test embed endpoint
try:
response = co.embed(
texts=["test connectivity"],
model="embed-english-v3.0"
)
print(f"Embed API: ✓ Working (latency: {response.meta['billed_units']['input_tokens']}ms)")
except Exception as e:
print(f"Embed API: ✗ Error - {str(e)}")
# Test generate endpoint
try:
response = co.generate(
prompt="Hello",
max_tokens=5
)
print("Generate API: ✓ Working")
except Exception as e:
print(f"Generate API: ✗ Error - {str(e)}")
Look for HTTP response codes outside the 2xx range, timeout errors, or connection failures.
4. Check Cohere Dashboard
If the Cohere Dashboard at dashboard.cohere.com is loading slowly or showing errors, this often indicates broader infrastructure issues. Pay attention to:
- Login failures or timeouts
- API key management access issues
- Usage metrics not loading
- Model playground unavailability
- Billing page errors
5. Monitor Community Channels
Check Cohere's community channels for real-time user reports:
- Cohere Discord - #support and #api-status channels
- Twitter/X @CohereAI - Official updates
- GitHub Issues - SDK-specific problems
- Developer forums and Reddit (r/MachineLearning)
Multiple users reporting the same issue simultaneously is a strong indicator of platform-wide problems.
Common Cohere Issues and How to Identify Them
Embed API Errors
Symptoms:
500 Internal Server ErrorresponsesConnection timeoutafter 30-60 secondsModel not founderrors for valid model names- Embedding dimension mismatches
- Slow response times (>5s for small batches)
What it means: The Embed API is Cohere's most heavily used service, powering semantic search, RAG pipelines, and recommendation systems. When embedding generation fails:
import cohere
co = cohere.Client('YOUR_API_KEY')
try:
# Batch embedding for efficiency
texts = [
"Document 1 content",
"Document 2 content",
"Document 3 content"
]
response = co.embed(
texts=texts,
model="embed-english-v3.0",
input_type="search_document"
)
embeddings = response.embeddings
print(f"Successfully generated {len(embeddings)} embeddings")
except cohere.CohereAPIError as e:
if e.status_code == 500:
print("Cohere Embed API experiencing server errors")
elif e.status_code == 503:
print("Cohere Embed API temporarily unavailable")
elif "timeout" in str(e).lower():
print("Embed API timeout - possible performance degradation")
else:
print(f"Embed API error: {e}")
except Exception as e:
print(f"Network or client error: {e}")
Common error patterns during outages:
- Consistent 500 errors across multiple requests
- Timeout exceptions after 60+ seconds
- Gateway errors (502, 503, 504)
- SSL/TLS handshake failures
Rerank API Failures
Symptoms:
- Rerank requests returning empty results
- 429 rate limit errors despite being under quota
- Relevance scores all returning as 0.0
- Timeout errors on large document sets
- Missing or malformed response fields
What it means: The Rerank API is critical for semantic search relevance. When it fails, search quality degrades significantly:
import cohere
co = cohere.Client('YOUR_API_KEY')
query = "What are the benefits of cloud computing?"
documents = [
"Cloud computing offers scalability and flexibility",
"The weather today is sunny",
"Cost reduction is a major cloud benefit",
"My favorite color is blue"
]
try:
response = co.rerank(
query=query,
documents=documents,
model="rerank-english-v3.0",
top_n=3
)
for idx, result in enumerate(response.results):
print(f"{idx+1}. Document {result.index}: {result.relevance_score:.4f}")
except cohere.CohereAPIError as e:
if e.status_code == 503:
print("Rerank API unavailable - falling back to vector similarity")
elif e.status_code == 429:
print("Rate limit exceeded (may indicate service degradation)")
else:
print(f"Rerank API error: {e}")
except TimeoutError:
print("Rerank timeout - possible performance issues")
Fallback strategy during outages:
def rerank_with_fallback(query, documents):
try:
# Try Cohere rerank first
response = co.rerank(query=query, documents=documents, top_n=5)
return response.results
except Exception as e:
print(f"Rerank failed, using vector similarity: {e}")
# Fallback to cosine similarity
query_embedding = co.embed(texts=[query], model="embed-english-v3.0").embeddings[0]
doc_embeddings = co.embed(texts=documents, model="embed-english-v3.0").embeddings
similarities = [
cosine_similarity(query_embedding, doc_emb)
for doc_emb in doc_embeddings
]
return sorted(enumerate(similarities), key=lambda x: x[1], reverse=True)[:5]
Chat/Generate Timeouts
Symptoms:
- Streaming responses stopping mid-generation
- 504 Gateway Timeout errors
- First token latency exceeding 10+ seconds
- Incomplete responses without proper ending tokens
- WebSocket connection drops
What it means: Chat and Generate APIs are compute-intensive. During outages or high load:
import cohere
import time
co = cohere.Client('YOUR_API_KEY')
def generate_with_retry(prompt, max_retries=3):
for attempt in range(max_retries):
try:
start_time = time.time()
response = co.chat(
message=prompt,
model="command-r-plus",
temperature=0.7,
max_tokens=500
)
latency = time.time() - start_time
# Monitor for degraded performance
if latency > 10:
print(f"⚠️ Slow response: {latency:.2f}s (attempt {attempt+1})")
return response.text
except cohere.CohereAPIError as e:
if e.status_code == 504:
print(f"Timeout on attempt {attempt+1}, retrying...")
time.sleep(2 ** attempt) # Exponential backoff
elif e.status_code == 503:
print("Service temporarily unavailable")
return None
else:
raise
except Exception as e:
print(f"Generation error: {e}")
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt)
return None
# Usage
result = generate_with_retry("Explain quantum computing in simple terms")
if result:
print(result)
else:
print("Generation failed after retries - Cohere may be experiencing issues")
Streaming with timeout detection:
import cohere
import time
co = cohere.Client('YOUR_API_KEY')
def stream_with_timeout(prompt, timeout=30):
try:
start_time = time.time()
last_chunk_time = start_time
stream = co.chat_stream(
message=prompt,
model="command-r-plus"
)
for event in stream:
if event.event_type == "text-generation":
current_time = time.time()
# Detect stalled streams
if current_time - last_chunk_time > timeout:
print("\n⚠️ Stream stalled - possible API degradation")
break
last_chunk_time = current_time
print(event.text, end='', flush=True)
except Exception as e:
elapsed = time.time() - start_time
print(f"\n✗ Stream failed after {elapsed:.2f}s: {e}")
Rate Limiting Issues
Symptoms:
- 429 status codes with
Retry-Afterheaders - Rate limit errors despite being under quota
- Inconsistent rate limit thresholds
- Trial API showing unexpected limits
What it means: During high load or outages, Cohere may implement aggressive rate limiting:
import cohere
import time
from datetime import datetime
co = cohere.Client('YOUR_API_KEY')
def embed_with_rate_limit_handling(texts, batch_size=96):
"""
Cohere allows up to 96 texts per embed request.
Handle rate limits gracefully with exponential backoff.
"""
all_embeddings = []
for i in range(0, len(texts), batch_size):
batch = texts[i:i + batch_size]
retries = 0
max_retries = 5
while retries < max_retries:
try:
response = co.embed(
texts=batch,
model="embed-english-v3.0",
input_type="search_document"
)
all_embeddings.extend(response.embeddings)
break
except cohere.CohereAPIError as e:
if e.status_code == 429:
# Extract retry-after header if available
retry_after = int(e.headers.get('Retry-After', 2 ** retries))
print(f"⚠️ Rate limited. Waiting {retry_after}s (attempt {retries+1}/{max_retries})")
time.sleep(retry_after)
retries += 1
if retries >= max_retries:
print("✗ Max retries exceeded - Cohere may be experiencing high load")
raise
else:
raise
return all_embeddings
# Monitor rate limit usage
def check_rate_limits():
"""Check current rate limit status"""
try:
# Make a minimal request to check headers
response = co.embed(texts=["test"], model="embed-english-v3.0")
# Check response headers for rate limit info
if hasattr(response, 'meta'):
print(f"API Usage: {response.meta}")
except Exception as e:
print(f"Rate limit check failed: {e}")
Authentication Issues
Symptoms:
- 401 Unauthorized errors with valid API keys
- "Invalid API key" messages
- Intermittent authentication failures
- Token validation timeouts
What it means: Authentication service issues can block all API access:
import cohere
import os
def validate_api_key(api_key=None):
"""Test API key validity and connectivity"""
if not api_key:
api_key = os.getenv('COHERE_API_KEY')
if not api_key:
return {
'valid': False,
'error': 'No API key provided'
}
try:
co = cohere.Client(api_key)
# Make minimal API call
response = co.embed(
texts=["test"],
model="embed-english-v3.0"
)
return {
'valid': True,
'status': 'Connected',
'billed_units': response.meta.get('billed_units', {})
}
except cohere.CohereAPIError as e:
if e.status_code == 401:
return {
'valid': False,
'error': 'Invalid API key or authentication failure',
'status_code': 401
}
elif e.status_code >= 500:
return {
'valid': None, # Unknown - server error
'error': 'Cohere server error - cannot verify key',
'status_code': e.status_code
}
else:
return {
'valid': False,
'error': str(e),
'status_code': e.status_code
}
except Exception as e:
return {
'valid': None,
'error': f'Connection error: {str(e)}'
}
# Usage
result = validate_api_key()
print(f"API Key Status: {result}")
The Real Impact When Cohere Goes Down
RAG Pipeline Failures
Retrieval-Augmented Generation (RAG) systems depend on Cohere for both embedding generation and reranking:
Impact cascade:
- New document ingestion stops - Cannot generate embeddings for new content
- Search quality degrades - Semantic search falls back to keyword matching
- User queries fail - Chat interfaces cannot retrieve relevant context
- Stale results - Users see outdated information without reranking
Example RAG system impact:
class RAGSystem:
def __init__(self):
self.co = cohere.Client(os.getenv('COHERE_API_KEY'))
self.vector_db = ChromaDB() # or Pinecone, Weaviate, etc.
def ingest_documents(self, documents):
"""Add new documents to knowledge base"""
try:
# Generate embeddings
embeddings = self.co.embed(
texts=documents,
model="embed-english-v3.0",
input_type="search_document"
).embeddings
# Store in vector DB
self.vector_db.add(documents, embeddings)
except Exception as e:
# During outage: Documents cannot be added
print(f"⚠️ Document ingestion failed: {e}")
# Queue for later processing
self.queue_for_retry(documents)
def query(self, question):
"""Answer user question using RAG"""
try:
# 1. Embed the question
query_embedding = self.co.embed(
texts=[question],
model="embed-english-v3.0",
input_type="search_query"
).embeddings[0]
# 2. Vector search for relevant docs
candidates = self.vector_db.search(query_embedding, top_k=20)
# 3. Rerank for precision
reranked = self.co.rerank(
query=question,
documents=[doc.text for doc in candidates],
model="rerank-english-v3.0",
top_n=5
).results
# 4. Generate answer with context
context = "\n".join([candidates[r.index].text for r in reranked])
answer = self.co.chat(
message=f"Context: {context}\n\nQuestion: {question}",
model="command-r-plus"
).text
return answer
except Exception as e:
print(f"⚠️ RAG pipeline failed: {e}")
# Graceful degradation
return "I'm experiencing technical difficulties. Please try again shortly."
# During Cohere outage, entire pipeline breaks down
For an enterprise RAG system handling 10,000 queries/hour, a 2-hour Cohere outage means:
- 20,000 failed user interactions
- Complete halt to knowledge base updates
- Support ticket surge
- Revenue impact for customer-facing AI features
Semantic Search Downtime
E-commerce, documentation, and content platforms rely on Cohere's embeddings for search:
Direct impacts:
- Users cannot find products/articles
- Search defaults to basic keyword matching (poor results)
- "No results found" increases dramatically
- User frustration and abandonment
Revenue implications:
- E-commerce: 30-40% of purchases start with search
- SaaS documentation: Poor search increases support tickets
- Content platforms: Reduced engagement and session duration
Enterprise AI Application Failures
Customer-facing AI features break:
- Chatbots cannot access knowledge bases
- AI writing assistants fail to generate content
- Recommendation engines stop updating
- Content moderation systems degrade
Internal AI tools impacted:
- Customer support AI assistance unavailable
- Internal search across company documents breaks
- Automated document processing halts
- AI-powered analytics stop updating
Multi-Tenant SaaS Platform Impact
If you're building an AI platform on Cohere:
# Example: Multi-tenant RAG platform
class MultiTenantAIPlatform:
def __init__(self):
self.co = cohere.Client(os.getenv('COHERE_API_KEY'))
def serve_customer_query(self, tenant_id, query):
"""
When Cohere is down, ALL customers are affected simultaneously
"""
try:
# Retrieve tenant's knowledge base
docs = self.get_tenant_docs(tenant_id)
# Embed query
query_emb = self.co.embed(texts=[query], model="embed-english-v3.0")
# Search and respond
results = self.vector_search(query_emb, docs)
return {
'success': True,
'results': results
}
except Exception as e:
# All tenants fail simultaneously
self.log_tenant_outage(tenant_id, e)
return {
'success': False,
'error': 'AI service temporarily unavailable'
}
Cascading effects:
- Hundreds or thousands of customers affected simultaneously
- Mass support ticket influx
- Social media complaints at scale
- Potential SLA breach penalties
- Churn risk from repeated outages
Cost and Resource Waste
During outages, your infrastructure continues running:
- Application servers idle, waiting for embeddings
- Database connections held open
- Queue workers consuming resources without progress
- Cloud compute costs continue while providing no value
- Engineering time spent troubleshooting instead of building
Incident Response Playbook: What to Do When Cohere Goes Down
1. Detect and Confirm Outage
Automated detection:
import cohere
import time
import requests
def check_cohere_health():
"""Comprehensive health check across all Cohere services"""
health_status = {
'timestamp': time.time(),
'services': {}
}
co = cohere.Client(os.getenv('COHERE_API_KEY'))
# Test Embed API
try:
start = time.time()
co.embed(texts=["health check"], model="embed-english-v3.0")
health_status['services']['embed'] = {
'status': 'operational',
'latency_ms': (time.time() - start) * 1000
}
except Exception as e:
health_status['services']['embed'] = {
'status': 'down',
'error': str(e)
}
# Test Rerank API
try:
start = time.time()
co.rerank(query="test", documents=["test doc"], model="rerank-english-v3.0")
health_status['services']['rerank'] = {
'status': 'operational',
'latency_ms': (time.time() - start) * 1000
}
except Exception as e:
health_status['services']['rerank'] = {
'status': 'down',
'error': str(e)
}
# Test Generate API
try:
start = time.time()
co.generate(prompt="test", max_tokens=5)
health_status['services']['generate'] = {
'status': 'operational',
'latency_ms': (time.time() - start) * 1000
}
except Exception as e:
health_status['services']['generate'] = {
'status': 'down',
'error': str(e)
}
# Check official status page
try:
status_response = requests.get('https://status.cohere.com/api/v2/status.json', timeout=5)
health_status['official_status'] = status_response.json()
except:
health_status['official_status'] = 'unavailable'
# Check API Status Check
try:
asc_response = requests.get('https://apistatuscheck.com/api/cohere', timeout=5)
health_status['apistatuscheck'] = asc_response.json()
except:
health_status['apistatuscheck'] = 'unavailable'
# Determine overall status
services_down = [s for s, data in health_status['services'].items() if data['status'] == 'down']
if len(services_down) == 0:
health_status['overall'] = 'operational'
elif len(services_down) < len(health_status['services']):
health_status['overall'] = 'degraded'
else:
health_status['overall'] = 'major_outage'
return health_status
# Run health check and alert if needed
status = check_cohere_health()
if status['overall'] != 'operational':
# Trigger incident response
alert_team(status)
2. Enable Fallback Mechanisms
Immediate actions:
class CohereWithFallbacks:
def __init__(self):
self.co = cohere.Client(os.getenv('COHERE_API_KEY'))
self.cache = CacheLayer() # Redis, Memcached, etc.
self.fallback_enabled = False
def embed_with_fallback(self, texts, model="embed-english-v3.0"):
"""Embedding with caching and fallback"""
# Check cache first
cached = self.cache.get_embeddings(texts, model)
if cached:
return cached
try:
# Try Cohere
response = self.co.embed(texts=texts, model=model)
# Cache successful response
self.cache.store_embeddings(texts, model, response.embeddings)
return response.embeddings
except Exception as e:
print(f"Cohere embed failed: {e}")
if self.fallback_enabled:
# Fallback to alternative provider
return self.fallback_embed_provider(texts)
else:
raise
def fallback_embed_provider(self, texts):
"""Fallback to OpenAI or HuggingFace"""
import openai
# Use OpenAI embeddings as fallback
response = openai.Embedding.create(
input=texts,
model="text-embedding-3-small"
)
return [item['embedding'] for item in response['data']]
def rerank_with_fallback(self, query, documents):
"""Rerank with vector similarity fallback"""
try:
return self.co.rerank(
query=query,
documents=documents,
model="rerank-english-v3.0"
).results
except Exception as e:
print(f"Cohere rerank failed, using cosine similarity: {e}")
# Fallback: compute similarity manually
query_emb = self.embed_with_fallback([query])[0]
doc_embs = self.embed_with_fallback(documents)
scores = [
self.cosine_similarity(query_emb, doc_emb)
for doc_emb in doc_embs
]
# Return in same format as Cohere
return [
{'index': idx, 'relevance_score': score}
for idx, score in sorted(enumerate(scores), key=lambda x: -x[1])
]
@staticmethod
def cosine_similarity(a, b):
import numpy as np
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
3. Implement Request Queuing
Queue failed operations for retry:
import json
from datetime import datetime
import redis
class CohereRequestQueue:
def __init__(self):
self.redis = redis.Redis(host='localhost', port=6379, db=0)
self.queue_key = 'cohere:failed_requests'
def queue_embed_request(self, texts, model, metadata=None):
"""Queue embedding request for later processing"""
request = {
'type': 'embed',
'texts': texts,
'model': model,
'metadata': metadata or {},
'queued_at': datetime.utcnow().isoformat()
}
self.redis.rpush(self.queue_key, json.dumps(request))
print(f"Queued embed request for {len(texts)} texts")
def queue_rerank_request(self, query, documents, metadata=None):
"""Queue rerank request for later processing"""
request = {
'type': 'rerank',
'query': query,
'documents': documents,
'metadata': metadata or {},
'queued_at': datetime.utcnow().isoformat()
}
self.redis.rpush(self.queue_key, json.dumps(request))
print(f"Queued rerank request for query: {query[:50]}...")
def process_queue(self):
"""Process queued requests when service is restored"""
co = cohere.Client(os.getenv('COHERE_API_KEY'))
processed = 0
failed = 0
while True:
# Get next request
request_json = self.redis.lpop(self.queue_key)
if not request_json:
break
request = json.loads(request_json)
try:
if request['type'] == 'embed':
co.embed(
texts=request['texts'],
model=request['model']
)
elif request['type'] == 'rerank':
co.rerank(
query=request['query'],
documents=request['documents']
)
processed += 1
except Exception as e:
print(f"Failed to process queued request: {e}")
# Re-queue with backoff
self.redis.rpush(self.queue_key, request_json)
failed += 1
if failed > 10: # Stop if still failing
print("Still experiencing issues, stopping queue processing")
break
return {
'processed': processed,
'failed': failed,
'remaining': self.redis.llen(self.queue_key)
}
# Usage during outage
queue = CohereRequestQueue()
try:
embeddings = co.embed(texts=documents, model="embed-english-v3.0")
except Exception:
# Queue for later
queue.queue_embed_request(documents, "embed-english-v3.0", metadata={'user_id': user_id})
# Return graceful error to user
return {'error': 'Processing delayed, will complete shortly'}
4. Communicate with Users
Status page update:
def update_status_page(status):
"""Update your application's status page"""
status_messages = {
'operational': 'All AI services operating normally',
'degraded': '⚠️ AI services experiencing delays - some features may be slow',
'major_outage': '🔴 AI services temporarily unavailable - we\'re working on it'
}
# Update status page
requests.post('https://your-status-page.com/api/update', json={
'component': 'AI Search & Recommendations',
'status': status,
'message': status_messages[status]
})
# Send notifications if degraded/down
if status != 'operational':
send_slack_alert(f"Cohere outage detected: {status}")
update_twitter(f"We're experiencing AI service delays due to a provider issue. Investigating now.")
User-facing messages:
def get_user_message(cohere_status):
"""Return appropriate user-facing message"""
if cohere_status == 'operational':
return None
elif cohere_status == 'degraded':
return {
'type': 'warning',
'message': 'Search results may be slower than usual. We\'re working to resolve this.',
'show_banner': True
}
else: # major_outage
return {
'type': 'error',
'message': 'AI-powered search is temporarily unavailable. Basic search is still available.',
'show_banner': True,
'fallback_action': 'Use basic search'
}
5. Monitor and Alert
Comprehensive monitoring setup:
import time
from datetime import datetime
import logging
class CohereMonitor:
def __init__(self):
self.co = cohere.Client(os.getenv('COHERE_API_KEY'))
self.alert_threshold = 3 # Alert after 3 consecutive failures
self.check_interval = 60 # Check every 60 seconds
self.failure_count = 0
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger('CohereMonitor')
def run_health_check(self):
"""Run health check and return status"""
try:
start = time.time()
# Test embed endpoint
self.co.embed(texts=["health check"], model="embed-english-v3.0")
latency = (time.time() - start) * 1000
# Reset failure count on success
if self.failure_count > 0:
self.logger.info(f"✓ Cohere recovered after {self.failure_count} failures")
self.send_recovery_alert()
self.failure_count = 0
# Alert on high latency
if latency > 5000: # 5 seconds
self.logger.warning(f"⚠️ High latency detected: {latency:.0f}ms")
return {
'status': 'healthy',
'latency_ms': latency,
'timestamp': datetime.utcnow().isoformat()
}
except Exception as e:
self.failure_count += 1
self.logger.error(f"✗ Health check failed (attempt {self.failure_count}): {e}")
# Send alert after threshold
if self.failure_count == self.alert_threshold:
self.send_outage_alert(e)
return {
'status': 'unhealthy',
'error': str(e),
'failure_count': self.failure_count,
'timestamp': datetime.utcnow().isoformat()
}
def send_outage_alert(self, error):
"""Send alert to team"""
alert_message = f"""
🚨 COHERE OUTAGE DETECTED
Failure count: {self.failure_count}
Error: {error}
Time: {datetime.utcnow().isoformat()}
Actions taken:
- Fallback mechanisms enabled
- User notifications sent
- Request queuing active
Check status:
- https://status.cohere.com
- https://apistatuscheck.com/api/cohere
"""
# Send to Slack/Discord/PagerDuty
self.send_slack_message(alert_message)
self.send_pagerduty_alert('Cohere API Outage', error)
def send_recovery_alert(self):
"""Send recovery notification"""
self.send_slack_message(f"✅ Cohere API recovered after {self.failure_count} failures")
def start_monitoring(self):
"""Start continuous monitoring"""
self.logger.info("Starting Cohere monitoring...")
while True:
status = self.run_health_check()
# Log to monitoring service
self.log_to_datadog(status)
time.sleep(self.check_interval)
# Run monitor
monitor = CohereMonitor()
monitor.start_monitoring()
6. Post-Outage Recovery
Checklist after service restoration:
def post_outage_recovery():
"""Run after Cohere service is restored"""
print("🔄 Starting post-outage recovery...")
# 1. Process queued requests
queue = CohereRequestQueue()
result = queue.process_queue()
print(f"✓ Processed {result['processed']} queued requests")
# 2. Verify all services operational
health = check_cohere_health()
if health['overall'] != 'operational':
print("⚠️ Warning: Some services still degraded")
return False
# 3. Disable fallback mode
config.set('cohere_fallback_enabled', False)
print("✓ Disabled fallback mechanisms")
# 4. Update status page
update_status_page('operational')
print("✓ Updated status page")
# 5. Generate incident report
report = generate_incident_report()
print(f"✓ Incident report: {report['url']}")
# 6. Notify team
send_slack_message("✅ Cohere outage resolved. All systems operational.")
return True
Frequently Asked Questions
How often does Cohere experience outages?
Cohere maintains high availability with typical uptime exceeding 99.9%. Major outages affecting all customers are rare (2-4 times per year), though specific model or regional issues may occur more frequently. Most production users experience minimal disruption. For real-time uptime tracking, check apistatuscheck.com/api/cohere.
What's the difference between Cohere and OpenAI embeddings?
Cohere's embed models are specifically optimized for semantic search and RAG applications, with features like separate input types (search_document vs search_query) and multilingual support. OpenAI's embeddings (text-embedding-3-small/large) offer excellent general-purpose performance. During Cohere outages, OpenAI can serve as a fallback, though you'll need to re-embed your document corpus. For a detailed comparison, see our OpenAI vs Cohere embeddings guide.
Can I use HuggingFace models as a Cohere alternative?
Yes, HuggingFace offers self-hosted embedding models like sentence-transformers that can serve as permanent or fallback solutions. Advantages: No API dependency, no rate limits, no costs after infrastructure. Disadvantages: Lower accuracy than Cohere's commercial models, requires GPU infrastructure, maintenance overhead. Learn more in our HuggingFace API status guide.
How do I prevent duplicate embeddings during retry logic?
Implement idempotent request handling by tracking document hashes or IDs. Before embedding, check if embeddings already exist in your vector database. Use unique identifiers for each document and store them with metadata. During retries, query by ID first to avoid duplicate processing.
What happens to my usage quota during outages?
Cohere typically does not charge for failed API requests. If requests time out or return 5xx errors, they should not count against your quota. However, if you've prepaid for usage, contact Cohere support for potential credits. Enterprise customers with SLAs may be eligible for service credits based on their agreement terms.
Should I cache Cohere embeddings?
Yes, absolutely. Embeddings for static content should always be cached in your vector database (Pinecone, Weaviate, ChromaDB, etc.). For dynamic content, implement a TTL-based cache (Redis, Memcached) to reduce API calls and maintain service during brief outages. Caching also significantly reduces costs and improves response times.
How do I monitor Cohere API performance in production?
Implement comprehensive monitoring:
- Track API response times (p50, p95, p99)
- Monitor error rates by endpoint
- Set up alerting for latency spikes or elevated errors
- Use API Status Check for external monitoring
- Subscribe to Cohere's status page updates
- Log all API interactions with timestamps and error details
What's the best fallback strategy for RAG systems?
Implement a multi-tiered fallback:
- Primary: Cohere embeddings + rerank
- Tier 1 fallback: Cached embeddings + cosine similarity (no rerank)
- Tier 2 fallback: OpenAI embeddings (requires re-embedding)
- Tier 3 fallback: BM25 keyword search
- Graceful degradation: Return relevant but unranked results with user notification
Test your fallback system regularly to ensure smooth transitions during actual outages.
How do I handle Cohere rate limits at scale?
Implement robust rate limiting:
- Respect
Retry-Afterheaders in 429 responses - Use exponential backoff (1s, 2s, 4s, 8s)
- Batch requests (up to 96 texts per embed call)
- Implement request queuing for burst traffic
- Consider upgrading to production tier for higher limits
- Distribute load across multiple API keys if allowed by your plan
Is there a Cohere status notification service?
Yes, several options:
- Subscribe at status.cohere.com for official updates via email
- Use API Status Check for automated monitoring with Slack/Discord/webhook alerts
- Monitor Cohere's Discord community for real-time user reports
- Follow @CohereAI on Twitter/X for incident updates
Stay Ahead of Cohere Outages
Don't let AI API downtime disrupt your RAG pipelines, semantic search, or enterprise AI applications. Subscribe to real-time Cohere alerts and get notified instantly when issues are detected—before your users notice.
API Status Check monitors Cohere 24/7 with:
- 60-second health checks across Embed, Rerank, Generate, and Chat APIs
- Instant alerts via email, Slack, Discord, or webhook
- Historical uptime tracking and incident reports
- Multi-model monitoring (embed-v3, rerank-v3, command-r-plus)
- Latency tracking and performance metrics
- Side-by-side comparison with OpenAI, Anthropic, and other AI providers
Last updated: February 4, 2026. Cohere status information is provided in real-time based on active monitoring. For official incident reports, always refer to status.cohere.com.
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